Information encoding in the brain is thought to be reflected in the pattern of activation of excitatory neurons in response to a given stimulus. This suggests that, in essence, a neural cell type is defined by the various stimuli and conditions that recruit its electrical activity. Alterations in activity in specific brain regions are associated wth a variety of neurological and psychiatric diseases and the pharmacological interventions to treat these diseases alter activity in specific circuits. The cellular and molecular changes that underli complex cognitive functions such as learning and memory are likely to occur at critical specific points in the circuits activated by the relevant stimuli. A great deal of effort in neuroscience is focused on defining these activated circuits however, currently available techniques are limited to discrete brain areas, lack cellular specificity, or provide a record of activity at only a singl time point preventing the identification of consistent patterns of network activation from noise or the identification of network changes over time in response to intervention. The approach that we will develop in this grant uses a single florescent marker to identify neural activity patterns t two independent time points. This provides a number of advantages over existing technology including, the ability to analyze the brain using high throughput automated imaging techniques, to identify specific cell populations in brain slices based on their activation patterns in the whoe animal for electrophysiological, morphological, or molecular studies, and the ability to apply FACS sorting techniques to the isolation of individual nuclei for epigenetic studies. The two time points at which activity is reported can be separated by at least one week allowing the analysis of circuit changes and target cell populations that are responsive to prolonged behavioral or pharmacological intervention. This should be useful in identifying the critical changes in the brain in response to these therapies.